1. Technical Field
The present invention relates to the field of automated systems and, more particularly, to interactive voice response systems.
2. Description of the Related Art
The cost of manned customer service centers has resulted in numerous companies utilizing automated interactive voice response (IVR) systems in place of or as a supplement to human agents. The IVR systems can perform customer service tasks, such as updating customer information. One routinely stored item of customer information is the customer's address. The customer's address can be necessary in order to ship purchased goods and to send postal mail messages, such as sale events and bills, to the customer.
Unfortunately, acquiring a customer address can be a difficult task for conventional IVR systems, which typically receive touch-tone keypad input and speech input. Notably, keypad input is an impractical manner for inputting customer address information due to the quantity of numbers and letters contained within an address. Therefore, IVR systems generally attempt to acquire customer address information from speech input using voice recognition technology.
Use of speech recognition for receiving address information, however, can be problematic. Problems with converting spoken addresses into text relate to unique characteristics of addresses. Specifically, addresses can be syntactically complex structures containing many different elements, such as cities, states, street names, digits, and the like. Accordingly, a speech recognition engine supporting address conversion requires a large recognition grammar in order to convert these different elements. Even so, the speech recognition engine can produce results for spoken addresses that contain many inaccuracies.
Speech recognition applications generally rely on the grammatical context of a speech input and on ordinary pronunciation rules to achieve accurate results. These two techniques are difficult to apply when converting spoken addresses to text. Many elements within an address, such as street name, are a hodgepodge of words having no significant grammatical context that can be used by speech recognition engines to differentiate among possible conversion alternatives. Further, speech recognition applications, which rely on ordinary pronunciation rules, often have difficulty converting proper nouns, which can contain a disproportionately large number of nonstandard pronunciation variations when compared to other words within a language. Of course, addresses can include many proper nouns, such as city and street names, that contain pronunciation exceptions. Accordingly, speech recognition accuracy for converting spoken addresses to text is generally low. As such, updating address information in computing systems has largely remained a manual process.